Discarding subjects before preprocessing. First, we use the following criteria for choosing the subjects to be preprocessed in the first place:
Let’s take a look at the reaction time distribution for each subject.
If we believe that the RT distribution tails reflect theoretically invalid processes, we could consider trimming them, i.e. discard trials whose RT’s are above or below some theoretical limit.
However, we actually have no strong motivation for this approach, since
Still, we visualise how many trials would be rejected based on such thresholds
## `summarise()` ungrouping output (override with `.groups` argument)
Results are given for each task x distractor state combination at the group and single subject levels. We will look at:
Speed
Accuracy
*Distractor effects are calculated in the following way:
\[\frac{HR_{distractors \ absent} - HR_{distractors \ present}} {HR_{distractors \ absent} + HR_{distractors \ present}}\]
## `summarise()` regrouping output by 'Group', 'dom_resp', 'ID', 'Task' (override with `.groups` argument)
## `summarise()` regrouping output by 'ID', 'dom_resp', 'Group', 'Task' (override with `.groups` argument)
## `summarise()` regrouping output by 'ID', 'dom_resp', 'Group', 'Task' (override with `.groups` argument)
## `summarise()` regrouping output by 'ID', 'dom_resp', 'Group', 'Task' (override with `.groups` argument)
## `summarise()` regrouping output by 'ID', 'dom_resp', 'Group', 'Task' (override with `.groups` argument)
Ex-Gaussian stats of Reaction time - mu (gaussian central estimate)
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: exgauss[Group == "Control" & dom_resp == FALSE, ]$mu and exgauss[Group == "Control" & dom_resp == TRUE, ]$mu
## D = 0.23333, p-value = 0.254
## alternative hypothesis: two-sided
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 2.95e+00 9.74e-02 8.33e-02
## 3 Task 2 54 8.90e-01 4.17e-01 2.58e-03
## 5 Distractors 1 27 5.47e+01 5.85e-08 * 7.37e-02
## 4 Group:Task 2 54 2.44e-01 7.84e-01 7.10e-04
## 6 Group:Distractors 1 27 1.86e-06 9.99e-01 2.69e-09
## 7 Task:Distractors 2 54 8.47e+00 6.32e-04 * 1.55e-02
## 8 Group:Task:Distractors 2 54 1.47e+00 2.38e-01 2.74e-03
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.861 0.1435
## 4 Group:Task 0.861 0.1435
## 7 Task:Distractors 0.808 0.0623
## 8 Group:Task:Distractors 0.808 0.0623
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF] p[HF]<.05
## 3 Task 0.878 0.40549 0.934 0.41087
## 4 Group:Task 0.878 0.75568 0.934 0.76943
## 7 Task:Distractors 0.839 0.00137 * 0.888 0.00108 *
## 8 Group:Task:Distractors 0.839 0.24002 0.888 0.23967
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.3497415
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.02739484 0.25083877
## Deg. of Freedom 1 27
##
## Residual standard error: 0.09638633
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.000771667 0.000214281 0.023690890
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.02094565
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.024009408 0.000000001 0.011826235
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.02092866
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.004629319 0.000827573 0.015172424
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01676218
## Estimated effects may be unbalanced
## `geom_smooth()` using formula 'y ~ x'
MU by LMM - joint tests and facet line plot of interactions
## model term df1 df2 F.ratio p.value
## Group 1 27 2.949 0.0974
## Distractors 1 27 54.749 <.0001
## Task 2 54 0.890 0.4167
## Group:Distractors 1 27 0.000 0.9989
## Group:Task 2 54 0.244 0.7842
## Distractors:Task 2 54 8.470 0.0006
## Group:Distractors:Task 2 54 1.473 0.2384
## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 26.452 <.0001
## Task 2 54 0.711 0.4955
## Distractors:Task 2 54 8.220 0.0008
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 28.363 <.0001
## Task 2 54 0.412 0.6643
## Distractors:Task 2 54 1.491 0.2343
## Distractors = Absent, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 1.054 0.3111
##
## Distractors = Present, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 3.224 0.0804
##
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 2.976 0.0925
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 1.838 0.1831
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 3.795 0.0587
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 38.69 2.392 0.1301
## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 29.54 2.817 0.1038
## Task 2 103.05 6.109 0.0031
## Group:Task 2 103.05 1.196 0.3066
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 29.54 2.815 0.1039
## Task 2 103.05 1.589 0.2090
## Group:Task 2 103.05 0.252 0.7779
## Task = AttendFull, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 41.507 <.0001
##
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 3.787 0.0553
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 3.324 0.0722
##
## Task = AttendFull, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 23.222 <.0001
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 8.426 0.0048
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 77.19 8.137 0.0056
## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 32.20 2.187 0.1489
## Distractors 1 77.19 63.708 <.0001
## Group:Distractors 1 77.19 1.652 0.2025
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 32.20 2.606 0.1162
## Distractors 1 77.19 11.672 0.0010
## Group:Distractors 1 77.19 0.381 0.5389
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 32.20 3.353 0.0763
## Distractors 1 77.19 10.845 0.0015
## Group:Distractors 1 77.19 0.450 0.5043
## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 103.05 5.965 0.0035
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 103.05 1.320 0.2715
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 103.05 1.175 0.3130
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 103.05 0.492 0.6128
Contrasts performed on the LMM of spread levels (all x all interaction)
## [1] "Control.Absent.AttendFull" "ADHD.Absent.AttendFull"
## [3] "Control.Present.AttendFull" "ADHD.Present.AttendFull"
## [5] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [7] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"
## TEST CONTRAST: FvLR_adhd 0 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001982
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2092 1 160 0.648
## TEST CONTRAST: LvFR_adhd 0 -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.004249
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.9617 1 160 0.3282
## TEST CONTRAST: RvFL_adhd 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.002267
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2738 1 160 0.6015
## TEST CONTRAST: dstr_adhd 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.0235
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 33.09 1 160 4.362e-08
## TEST CONTRAST: drAF_adhd 0 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.006471
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.23 1 160 0.1373
## TEST CONTRAST: drAL_adhd 0 -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.00311
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5152 1 160 0.4739
## TEST CONTRAST: drAR_adhd 0 -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.003361
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.6016 1 160 0.4391
## TEST CONTRAST: FvLR_ctrl 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005715
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.623 1 160 0.2045
## TEST CONTRAST: LvFR_ctrl -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.003626
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.6538 1 160 0.42
## TEST CONTRAST: RvFL_ctrl -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002088
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2168 1 160 0.6422
## TEST CONTRAST: dstr_ctrl 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.02349
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 30.86 1 160 1.134e-07
## TEST CONTRAST: drAF_ctrl 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.01573
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 12.3 1 160 0.0005895
## TEST CONTRAST: drAL_ctrl -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.007545
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.83 1 160 0.09447
## TEST CONTRAST: drAR_ctrl -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.008181
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 3.328 1 160 0.06999
## TEST CONTRAST: drAF_ctrl V ADHD 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.004628
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.203 1 160 0.1397
## TEST CONTRAST: drAL_ctrl V ADHD -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002217
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5057 1 160 0.478
## TEST CONTRAST: drAR_ctrl V ADHD -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.00241
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5976 1 160 0.4406
Ex-Gaussian stats of Reaction time - sigma (gaussian dispersion)
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: exgauss[Group == "Control" & dom_resp == FALSE, ]$sigma and exgauss[Group == "Control" & dom_resp == TRUE, ]$sigma
## D = 0.2381, p-value = 0.2342
## alternative hypothesis: two-sided
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 4.372677168 0.04606248 * 9.189650e-02
## 3 Task 2 54 1.249324307 0.29485187 8.298936e-03
## 5 Distractors 1 27 5.795958889 0.02316748 * 2.348284e-02
## 4 Group:Task 2 54 0.138527943 0.87094732 9.270455e-04
## 6 Group:Distractors 1 27 0.001054741 0.97433074 4.376120e-06
## 7 Task:Distractors 2 54 2.065145049 0.13669867 6.252890e-03
## 8 Group:Task:Distractors 2 54 1.097591422 0.34100137 3.333075e-03
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.9994394 0.9927361
## 4 Group:Task 0.9994394 0.9927361
## 7 Task:Distractors 0.9754472 0.7238507
## 8 Group:Task:Distractors 0.9754472 0.7238507
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 Task 0.9994397 0.2948431 1.079324 0.2948519
## 4 Group:Task 0.9994397 0.8708397 1.079324 0.8709473
## 7 Task:Distractors 0.9760356 0.1380059 1.051145 0.1366987
## 8 Group:Task:Distractors 0.9760356 0.3399814 1.051145 0.3410014
## p[HF]<.05
## 3
## 4
## 7
## 8
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.05401102
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.00733327 0.04528077
## Deg. of Freedom 1 27
##
## Residual standard error: 0.04095199
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.000604827 0.000067242 0.013105816
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01557884
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.001743079 0.000000317 0.008117886
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.01733962
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.000434113 0.000242342 0.005961450
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01050701
## Estimated effects may be unbalanced
## `geom_smooth()` using formula 'y ~ x'
SIGMA by LMM - joint tests and facet line plot of interactions
## model term df1 df2 F.ratio p.value
## Group 1 27 4.373 0.0461
## Distractors 1 27 5.796 0.0232
## Task 2 54 1.249 0.2949
## Group:Distractors 1 27 0.001 0.9743
## Group:Task 2 54 0.139 0.8709
## Distractors:Task 2 54 2.065 0.1367
## Group:Distractors:Task 2 54 1.098 0.3410
## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 2.878 0.1013
## Task 2 54 0.740 0.4819
## Distractors:Task 2 54 2.967 0.0599
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 2.921 0.0989
## Task 2 54 0.645 0.5289
## Distractors:Task 2 54 0.096 0.9083
## Distractors = Absent, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 1.248 0.2682
##
## Distractors = Present, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 3.749 0.0573
##
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 3.754 0.0571
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 3.293 0.0743
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 3.503 0.0658
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 63.87 1.539 0.2193
## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 36.38 3.659 0.0637
## Task 2 94.70 2.956 0.0569
## Group:Task 2 94.70 0.527 0.5922
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 36.38 3.757 0.0604
## Task 2 94.70 0.053 0.9485
## Group:Task 2 94.70 0.350 0.7057
## Task = AttendFull, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 7.941 0.0064
##
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 0.779 0.3806
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 0.027 0.8707
##
## Task = AttendFull, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 2.494 0.1192
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 1.242 0.2693
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 63.97 1.439 0.2346
## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 43.09 2.892 0.0962
## Distractors 1 63.97 9.758 0.0027
## Group:Distractors 1 63.97 0.864 0.3561
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 43.09 4.369 0.0425
## Distractors 1 63.97 1.986 0.1636
## Group:Distractors 1 63.97 0.019 0.8896
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 43.09 3.005 0.0901
## Distractors 1 63.97 0.905 0.3451
## Group:Distractors 1 63.97 0.513 0.4766
## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 94.7 2.797 0.0660
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 94.7 0.076 0.9271
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 94.7 0.610 0.5453
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 94.7 0.336 0.7154
Contrasts performed on the LMM of spread levels (all x all interaction)
## [1] "Control.Absent.AttendFull" "ADHD.Absent.AttendFull"
## [3] "Control.Present.AttendFull" "ADHD.Present.AttendFull"
## [5] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [7] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"
## TEST CONTRAST: FvLR_adhd 0 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.002862
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.8135 1 160 0.3685
## TEST CONTRAST: LvFR_adhd 0 -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.0009336
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.08657 1 160 0.769
## TEST CONTRAST: RvFL_adhd 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.003795
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.431 1 160 0.2334
## TEST CONTRAST: dstr_adhd 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.006248
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 4.361 1 160 0.03835
## TEST CONTRAST: drAF_adhd 0 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001016
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.1025 1 160 0.7493
## TEST CONTRAST: drAL_adhd 0 -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0006619
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.04352 1 160 0.835
## TEST CONTRAST: drAR_adhd 0 -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0003538
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.01243 1 160 0.9114
## TEST CONTRAST: FvLR_ctrl 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.004352
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.755 1 160 0.1871
## TEST CONTRAST: LvFR_ctrl -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001698
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2672 1 160 0.6059
## TEST CONTRAST: RvFL_ctrl -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002654
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.6529 1 160 0.4203
## TEST CONTRAST: dstr_ctrl 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.006419
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 4.296 1 160 0.0398
## TEST CONTRAST: drAF_ctrl 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005718
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 3.03 1 160 0.08364
## TEST CONTRAST: drAL_ctrl -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001515
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2126 1 160 0.6453
## TEST CONTRAST: drAR_ctrl -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.004203
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.637 1 160 0.2025
## TEST CONTRAST: drAF_ctrl V ADHD 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.002351
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.06 1 160 0.3048
## TEST CONTRAST: drAL_ctrl V ADHD -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0004263
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.03486 1 160 0.8521
## TEST CONTRAST: drAR_ctrl V ADHD -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001925
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.7104 1 160 0.4006
Ex-Gaussian stats of Reaction time - tau (exponential tail)
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: exgauss[Group == "Control" & dom_resp == FALSE, ]$tau and exgauss[Group == "Control" & dom_resp == TRUE, ]$tau
## D = 0.1619, p-value = 0.6877
## alternative hypothesis: two-sided
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 0.07631259 0.7844617 0.0010830259
## 3 Task 2 54 1.39517295 0.2565781 0.0089559367
## 5 Distractors 1 27 0.07716397 0.7832942 0.0004362719
## 4 Group:Task 2 54 0.62658427 0.5382542 0.0040421317
## 6 Group:Distractors 1 27 0.48632417 0.4915334 0.0027432474
## 7 Task:Distractors 2 54 0.80645202 0.4517426 0.0085521564
## 8 Group:Task:Distractors 2 54 0.37485044 0.6891666 0.0039934426
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.9418604 0.4590133
## 4 Group:Task 0.9418604 0.4590133
## 7 Task:Distractors 0.9659766 0.6376254
## 8 Group:Task:Distractors 0.9659766 0.6376254
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 Task 0.9450549 0.2567726 1.014004 0.2565781
## 4 Group:Task 0.9450549 0.5297651 1.014004 0.5382542
## 7 Task:Distractors 0.9670961 0.4482268 1.040409 0.4517426
## 8 Group:Task:Distractors 0.9670961 0.6822427 1.040409 0.6891666
## p[HF]<.05
## 3
## 4
## 7
## 8
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.08739718
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.000059174 0.020936263
## Deg. of Freedom 1 27
##
## Residual standard error: 0.02784631
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.000508445 0.000221509 0.009544999
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01329508
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.000019899 0.000150134 0.008335238
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.01757022
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.000493742 0.000218830 0.015762064
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01708479
## Estimated effects may be unbalanced
## boundary (singular) fit: see ?isSingular
## `geom_smooth()` using formula 'y ~ x'
TAU by LMM - joint tests and facet line plot of interactions
## model term df1 df2 F.ratio p.value
## Group 1 27 0.076 0.7845
## Distractors 1 27 0.077 0.7833
## Task 2 54 1.052 0.3561
## Group:Distractors 1 27 0.486 0.4915
## Group:Task 2 54 0.473 0.6259
## Distractors:Task 2 54 1.005 0.3729
## Group:Distractors:Task 2 54 0.467 0.6294
## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 0.460 0.5036
## Task 2 54 0.308 0.7364
## Distractors:Task 2 54 0.050 0.9512
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 0.091 0.7650
## Task 2 54 1.250 0.2947
## Distractors:Task 2 54 1.470 0.2389
## Distractors = Absent, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.780 0.3788
##
## Distractors = Present, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.202 0.6540
##
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.023 0.8796
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.184 0.6690
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.534 0.4663
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 120.42 0.205 0.6514
## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 45.56 0.019 0.8905
## Task 2 108.00 1.742 0.1801
## Group:Task 2 108.00 0.939 0.3941
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 45.56 0.367 0.5477
## Task 2 108.00 0.315 0.7303
## Group:Task 2 108.00 0.000 0.9998
## Task = AttendFull, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.040 0.8417
##
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.211 0.6476
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.388 0.5354
##
## Task = AttendFull, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 1.793 0.1843
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.039 0.8431
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 79.54 0.935 0.3365
## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 58.77 0.077 0.7831
## Distractors 1 79.54 0.618 0.4340
## Group:Distractors 1 79.54 1.155 0.2858
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 58.77 0.031 0.8606
## Distractors 1 79.54 0.037 0.8482
## Group:Distractors 1 79.54 0.219 0.6411
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 58.77 0.569 0.4536
## Distractors 1 79.54 1.254 0.2663
## Group:Distractors 1 79.54 0.050 0.8232
## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.197 0.8214
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.161 0.8517
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 108 2.566 0.0815
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.154 0.8570
Contrasts performed on the LMM of spread levels (all x all interaction)
## [1] "Control.Absent.AttendFull" "ADHD.Absent.AttendFull"
## [3] "Control.Present.AttendFull" "ADHD.Present.AttendFull"
## [5] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [7] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"
## TEST CONTRAST: FvLR_adhd 0 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005404
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.343 1 160 0.1278
## TEST CONTRAST: LvFR_adhd 0 -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002444
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.4795 1 160 0.4897
## TEST CONTRAST: RvFL_adhd 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002959
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.7028 1 160 0.4031
## TEST CONTRAST: dstr_adhd 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001118
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.1129 1 160 0.7373
## TEST CONTRAST: drAF_adhd 0 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005065
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.059 1 160 0.1533
## TEST CONTRAST: drAL_adhd 0 -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -3.676e-05
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.0001085 1 160 0.9917
## TEST CONTRAST: drAR_adhd 0 -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.005102
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.089 1 160 0.1503
## TEST CONTRAST: FvLR_ctrl 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001433
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.1538 1 160 0.6954
## TEST CONTRAST: LvFR_ctrl -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002779
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5785 1 160 0.448
## TEST CONTRAST: RvFL_ctrl -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001346
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.1357 1 160 0.7131
## TEST CONTRAST: dstr_ctrl 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002599
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5694 1 160 0.4516
## TEST CONTRAST: drAF_ctrl 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001035
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.0803 1 160 0.7773
## TEST CONTRAST: drAL_ctrl -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0001441
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.001556 1 160 0.9686
## TEST CONTRAST: drAR_ctrl -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0008913
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.0595 1 160 0.8076
## TEST CONTRAST: drAF_ctrl V ADHD 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.002015
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.6291 1 160 0.4289
## TEST CONTRAST: drAL_ctrl V ADHD -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 9.045e-05
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.001268 1 160 0.9716
## TEST CONTRAST: drAR_ctrl V ADHD -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.002105
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.6868 1 160 0.4085
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: hit_rates[Group == "Control" & dom_resp == FALSE, ]$hit_rate and hit_rates[Group == "Control" & dom_resp == TRUE, ]$hit_rate
## D = 0.48095, p-value = 0.0006095
## alternative hypothesis: two-sided
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 1.184201e-04 9.913975e-01 3.503582e-06
## 3 Task 2 54 4.150472e-01 6.623975e-01 1.069931e-03
## 5 Distractors 1 27 7.184181e+01 4.342724e-09 * 1.416731e-01
## 4 Group:Task 2 54 4.748406e-02 9.536654e-01 1.225230e-04
## 6 Group:Distractors 1 27 2.126028e-03 9.635628e-01 4.884545e-06
## 7 Task:Distractors 2 54 3.916128e+00 2.581213e-02 * 9.974818e-03
## 8 Group:Task:Distractors 2 54 2.198191e+00 1.208401e-01 5.623646e-03
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.9639195 0.6201978
## 4 Group:Task 0.9639195 0.6201978
## 7 Task:Distractors 0.9009637 0.2577478
## 8 Group:Task:Distractors 0.9009637 0.2577478
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 Task 0.9651760 0.65531325 1.0381051 0.66239749
## 4 Group:Task 0.9651760 0.94946032 1.0381051 0.95366544
## 7 Task:Distractors 0.9098881 0.02989337 * 0.9720671 0.02701283
## 8 Group:Task:Distractors 0.9098881 0.12613623 0.9720671 0.12246783
## p[HF]<.05
## 3
## 4
## 7 *
## 8
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.8621807
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.0000043 0.9758254
## Deg. of Freedom 1 27
##
## Residual standard error: 0.1901097
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.00132633 0.00014969 0.08511514
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.03970147
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.20179408 0.00000597 0.07577765
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.05297716
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.01173242 0.00690856 0.08485660
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.03964112
## Estimated effects may be unbalanced
## `geom_smooth()` using formula 'y ~ x'
Hit rate by LMM - joint tests and facet line plot of interactions
## model term df1 df2 F.ratio p.value
## Group 1 27 0.000 0.9914
## Distractors 1 27 71.837 <.0001
## Task 2 54 0.416 0.6620
## Group:Distractors 1 27 0.002 0.9636
## Group:Task 2 54 0.048 0.9536
## Distractors:Task 2 54 3.910 0.0259
## Group:Distractors:Task 2 54 2.195 0.1212
## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 35.100 <.0001
## Task 2 54 0.152 0.8595
## Distractors:Task 2 54 5.612 0.0061
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 36.797 <.0001
## Task 2 54 0.317 0.7296
## Distractors:Task 2 54 0.311 0.7342
## Distractors = Absent, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.215 0.6454
##
## Distractors = Present, Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.393 0.5344
##
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.019 0.8913
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.015 0.9022
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.103 0.7500
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 41.74 0.193 0.6627
## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 31.17 0.000 0.9985
## Task 2 108.00 3.318 0.0399
## Group:Task 2 108.00 0.806 0.4492
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 31.17 0.001 0.9819
## Task 2 108.00 1.008 0.3684
## Group:Task 2 108.00 1.436 0.2424
## Task = AttendFull, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 42.230 <.0001
##
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 8.918 0.0038
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 7.388 0.0082
##
## Task = AttendFull, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 20.833 <.0001
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 13.026 0.0006
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 74.6 18.672 <.0001
## Task = AttendFull:
## model term df1 df2 F.ratio p.value
## Group 1 31.79 0.008 0.9308
## Distractors 1 74.60 61.543 <.0001
## Group:Distractors 1 74.60 2.257 0.1372
##
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 31.79 0.000 0.9941
## Distractors 1 74.60 21.672 <.0001
## Group:Distractors 1 74.60 0.130 0.7199
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 31.79 0.004 0.9497
## Distractors 1 74.60 24.575 <.0001
## Group:Distractors 1 74.60 1.097 0.2983
## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 108 3.439 0.0356
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 108 2.324 0.1027
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.587 0.5578
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 108 0.041 0.9600
Contrasts performed on the LMM of spread levels (all x all interaction)
## [1] "Control.Absent.AttendFull" "ADHD.Absent.AttendFull"
## [3] "Control.Present.AttendFull" "ADHD.Present.AttendFull"
## [5] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [7] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"
## TEST CONTRAST: FvLR_adhd 0 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.006781
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5052 1 160 0.4783
## TEST CONTRAST: LvFR_adhd 0 -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005105
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2863 1 160 0.5933
## TEST CONTRAST: RvFL_adhd 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001676
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.03086 1 160 0.8608
## TEST CONTRAST: dstr_adhd 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.06775
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 56.74 1 160 3.451e-12
## TEST CONTRAST: drAF_adhd 0 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.004873
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2609 1 160 0.6102
## TEST CONTRAST: drAL_adhd 0 -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.00678
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.5051 1 160 0.4783
## TEST CONTRAST: drAR_adhd 0 -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001907
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.03996 1 160 0.8418
## TEST CONTRAST: FvLR_ctrl 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.003306
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.1121 1 160 0.7382
## TEST CONTRAST: LvFR_ctrl -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.004971
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2534 1 160 0.6154
## TEST CONTRAST: RvFL_ctrl -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001665
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.02843 1 160 0.8663
## TEST CONTRAST: dstr_ctrl 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.06849
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 54.12 1 160 9.275e-12
## TEST CONTRAST: drAF_ctrl 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0307
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 9.664 1 160 0.002225
## TEST CONTRAST: drAL_ctrl -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.01366
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.912 1 160 0.1686
## TEST CONTRAST: drAR_ctrl -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.01704
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.978 1 160 0.08632
## TEST CONTRAST: drAF_ctrl V ADHD 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.01291
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 3.537 1 160 0.06182
## TEST CONTRAST: drAL_ctrl V ADHD -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.003438
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2508 1 160 0.6172
## TEST CONTRAST: drAR_ctrl V ADHD -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.009475
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.905 1 160 0.1695
## `summarise()` regrouping output by 'Group', 'Task' (override with `.groups` argument)
##
## Two-sample Kolmogorov-Smirnov test
##
## data: fa_rates[Group == "Control" & dom_resp == FALSE, ]$fa_rate and fa_rates[Group == "Control" & dom_resp == TRUE, ]$fa_rate
## D = 0.16667, p-value = 0.7159
## alternative hypothesis: two-sided
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 0.99624544 0.3270825057 0.0235676226
## 3 Task 2 54 1.47716918 0.2373600463 0.0082416656
## 5 Distractors 1 27 15.43837908 0.0005336195 * 0.0529696664
## 4 Group:Task 2 54 0.72569044 0.4886497069 0.0040659392
## 6 Group:Distractors 1 27 0.03775998 0.8473805867 0.0001367836
## 7 Task:Distractors 2 54 9.37433253 0.0003201517 * 0.0323030917
## 8 Group:Task:Distractors 2 54 0.04387023 0.9571122216 0.0001561948
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.9653826 0.6325478
## 4 Group:Task 0.9653826 0.6325478
## 7 Task:Distractors 0.9583853 0.5754691
## 8 Group:Task:Distractors 0.9583853 0.5754691
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF]
## 3 Task 0.9665409 0.2378270702 1.039743 0.2373600463
## 4 Group:Task 0.9665409 0.4843929958 1.039743 0.4886497069
## 7 Task:Distractors 0.9600479 0.0003975696 * 1.031955 0.0003201517
## 8 Group:Task:Distractors 0.9600479 0.9524710628 1.031955 0.9571122216
## p[HF]<.05
## 3
## 4
## 7 *
## 8
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.9771982
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.00274685 0.07444458
## Deg. of Freedom 1 27
##
## Residual standard error: 0.05250911
## 5 out of 6 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.000904047 0.000464614 0.017286406
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.01789186
## 4 out of 8 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 3: ID:Distractors
##
## Terms:
## Distractors Group:Distractors Residuals
## Sum of Squares 0.006351256 0.000015569 0.011132366
## Deg. of Freedom 1 1 27
##
## Residual standard error: 0.02030541
## 4 out of 6 effects not estimable
## Estimated effects may be unbalanced
##
## Stratum 4: ID:Task:Distractors
##
## Terms:
## Task:Distractors Group:Task:Distractors Residuals
## Sum of Squares 0.003792034 0.000017779 0.010941836
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.0142347
## Estimated effects may be unbalanced
## `geom_smooth()` using formula 'y ~ x'
False Alarm by LMM - joint tests and facet line plot of interactions
## model term df1 df2 F.ratio p.value
## Group 1 27 0.996 0.3271
## Distractors 1 27 15.438 0.0005
## Task 2 54 1.477 0.2374
## Group:Distractors 1 27 0.038 0.8474
## Group:Task 2 54 0.726 0.4887
## Distractors:Task 2 54 9.374 0.0003
## Group:Distractors:Task 2 54 0.044 0.9571
## Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 8.218 0.0079
## Task 2 54 2.013 0.1435
## Distractors:Task 2 54 4.949 0.0106
##
## Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 27 7.224 0.0122
## Task 2 54 0.125 0.8831
## Distractors:Task 2 54 4.452 0.0162
## Distractors = Absent, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 59.52 0.587 0.4467
##
## Distractors = Present, Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 59.52 0.218 0.6425
##
## Distractors = Absent, Task = AttendNone:
## model term df1 df2 F.ratio p.value
## Group 1 59.52 1.615 0.2087
##
## Distractors = Present, Task = AttendNone:
## model term df1 df2 F.ratio p.value
## Group 1 59.52 1.632 0.2063
##
## Distractors = Absent, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 59.52 0.322 0.5727
##
## Distractors = Present, Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 59.52 0.245 0.6222
## Distractors = Absent:
## model term df1 df2 F.ratio p.value
## Group 1 34.90 1.002 0.3237
## Task 2 102.81 1.133 0.3260
## Group:Task 2 102.81 0.354 0.7030
##
## Distractors = Present:
## model term df1 df2 F.ratio p.value
## Group 1 34.90 0.741 0.3952
## Task 2 102.81 7.943 0.0006
## Group:Task 2 102.81 0.569 0.5677
## Task = AttendLeft, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 71.58 9.535 0.0029
##
## Task = AttendNone, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 71.58 0.032 0.8590
##
## Task = AttendRight, Group = Control:
## model term df1 df2 F.ratio p.value
## Distractors 1 71.58 10.227 0.0021
##
## Task = AttendLeft, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 71.58 7.327 0.0085
##
## Task = AttendNone, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 71.58 0.030 0.8628
##
## Task = AttendRight, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Distractors 1 71.58 10.193 0.0021
## Task = AttendLeft:
## model term df1 df2 F.ratio p.value
## Group 1 39.92 0.471 0.4964
## Distractors 1 71.58 16.822 0.0001
## Group:Distractors 1 71.58 0.116 0.7348
##
## Task = AttendNone:
## model term df1 df2 F.ratio p.value
## Group 1 39.92 2.014 0.1636
## Distractors 1 71.58 0.062 0.8043
## Group:Distractors 1 71.58 0.000 0.9938
##
## Task = AttendRight:
## model term df1 df2 F.ratio p.value
## Group 1 39.92 0.350 0.5573
## Distractors 1 71.58 20.414 <.0001
## Group:Distractors 1 71.58 0.007 0.9351
## Distractors = Absent, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 102.81 0.185 0.8315
##
## Distractors = Present, Group = Control:
## model term df1 df2 F.ratio p.value
## Task 2 102.81 6.117 0.0031
##
## Distractors = Absent, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 102.81 1.342 0.2659
##
## Distractors = Present, Group = ADHD:
## model term df1 df2 F.ratio p.value
## Task 2 102.81 2.262 0.1093
Contrasts performed on the LMM of spread levels (all x all interaction)
## [1] "Control.Absent.AttendLeft" "ADHD.Absent.AttendLeft"
## [3] "Control.Present.AttendLeft" "ADHD.Present.AttendLeft"
## [5] "Control.Absent.AttendNone" "ADHD.Absent.AttendNone"
## [7] "Control.Present.AttendNone" "ADHD.Present.AttendNone"
## [9] "Control.Absent.AttendRight" "ADHD.Absent.AttendRight"
## [11] "Control.Present.AttendRight" "ADHD.Present.AttendRight"
## TEST CONTRAST: NvLR_adhd 0 -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.001049
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.07544 1 160 0.7839
## TEST CONTRAST: LvNR_adhd 0 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.0009479
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.06164 1 160 0.8042
## TEST CONTRAST: RvNL_adhd 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001997
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2735 1 160 0.6017
## TEST CONTRAST: dstr_adhd 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.01151
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 10.22 1 160 0.001678
## TEST CONTRAST: drAN_adhd 0 -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.009413
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 6.078 1 160 0.01474
## TEST CONTRAST: drAL_adhd 0 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.003608
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.8931 1 160 0.3461
## TEST CONTRAST: drAR_adhd 0 -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005805
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.311 1 160 0.1304
## TEST CONTRAST: NvLR_ctrl -0.25 0 -0.25 0 0.5 0 0.5 0 -0.25 0 -0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.007948
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 4.044 1 160 0.04601
## TEST CONTRAST: LvNR_ctrl 0.5 0 0.5 0 -0.25 0 -0.25 0 -0.25 0 -0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.001874
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.2247 1 160 0.6361
## TEST CONTRAST: RvNL_ctrl -0.25 0 -0.25 0 -0.25 0 -0.25 0 0.5 0 0.5 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.006074
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 2.362 1 160 0.1263
## TEST CONTRAST: dstr_ctrl 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0 0.3333333 0 -0.3333333 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.0127
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 11.62 1 160 0.0008249
## TEST CONTRAST: drAN_ctrl -0.25 0 0.25 0 0.5 0 -0.5 0 -0.25 0 0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.01036
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 6.873 1 160 0.009594
## TEST CONTRAST: drAL_ctrl 0.5 0 -0.5 0 -0.25 0 0.25 0 -0.25 0 0.25 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.004923
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.552 1 160 0.2147
## TEST CONTRAST: drAR_ctrl -0.25 0 0.25 0 -0.25 0 0.25 0 0.5 0 -0.5 0
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.005438
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 1.893 1 160 0.1707
## TEST CONTRAST: drAN_ctrl V ADHD -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0004741
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.02978 1 160 0.8632
## TEST CONTRAST: drAL_ctrl V ADHD 0.25 -0.25 -0.25 0.25 -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 -0.0006574
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.05724 1 160 0.8112
## TEST CONTRAST: drAR_ctrl V ADHD -0.125 0.125 0.125 -0.125 -0.125 0.125 0.125 -0.125 0.25 -0.25 -0.25 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## contrast == 0 0.0001832
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.004446 1 160 0.9469
## `summarise()` regrouping output by 'Group' (override with `.groups` argument)
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 2 Group 1 27 0.000181595 0.9893473 3.337908e-06
## 3 Task 2 54 3.703857225 0.0310894 * 6.463280e-02
## 4 Group:Task 2 54 2.241067418 0.1161461 4.013131e-02
##
## $`Mauchly's Test for Sphericity`
## Effect W p p<.05
## 3 Task 0.8796146 0.1887128
## 4 Group:Task 0.8796146 0.1887128
##
## $`Sphericity Corrections`
## Effect GGe p[GG] p[GG]<.05 HFe p[HF] p[HF]<.05
## 3 Task 0.89255 0.0364544 * 0.9514771 0.0334055 *
## 4 Group:Task 0.89255 0.1225835 0.9514771 0.1190267
##
## $aov
##
## Call:
## aov(formula = formula(aov_formula), data = data)
##
## Grand Mean: 0.04157256
##
## Stratum 1: ID
##
## Terms:
## Group Residuals
## Sum of Squares 0.00000044 0.06523006
## Deg. of Freedom 1 27
##
## Residual standard error: 0.04915209
## 2 out of 3 effects not estimable
## Estimated effects are balanced
##
## Stratum 2: ID:Task
##
## Terms:
## Task Group:Task Residuals
## Sum of Squares 0.00864155 0.00549520 0.06620525
## Deg. of Freedom 2 2 54
##
## Residual standard error: 0.03501461
## Estimated effects may be unbalanced
## [1] "Control.AttendFull" "ADHD.AttendFull" "Control.AttendLeft"
## [4] "ADHD.AttendLeft" "Control.AttendRight" "ADHD.AttendRight"
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## DE_group == 0 0.0004263
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 0.0001816 1 79 0.9893
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## FvLR_ctrl == 0 0.03795
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 10.96 1 79 0.001403
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## RvFL_ctrl == 0 -0.02008
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 3.069 1 79 0.0837
## TEST CONTRAST: FvLR ctrl V ADHD 0.5 -0.5 -0.25 0.25 -0.25 0.25
##
## General Linear Hypotheses
##
## Multiple Comparisons of Means: User-defined Contrasts
##
##
## Linear Hypotheses:
## Estimate
## FvLR_adhd == 0 0.01642
##
## Global Test:
## F DF1 DF2 Pr(>F)
## 1 4.245 1 79 0.04265
There was no effect of group on Distractor effect. Task = attend-Full differed strongly from Left/Right for CTRL group (F = 11), and this induced a between groups difference with ADHD in the same Task contrast (F = 4.2)